Accelerating prostate MRI: a practical review of modern acquisition strategies

Abdelrahman Elshikh1,2, Eugene Milshteyn3, Nabih Nakrour1,2

  • 1Massachusetts General Hospital, Boston, USA.

Insights

Accelerated prostate MRI protocols can significantly reduce scan times, improving patient comfort and image quality. Innovations like deep learning reconstruction offer efficient prostate cancer detection while maintaining diagnostic standards.

Area of Science:

  • Radiology
  • Medical Imaging
  • Oncology

Background:

  • Prostate MRI is crucial for prostate cancer management, but conventional protocols exceed 30 minutes.
  • Longer scan times lead to patient motion, reduced image quality, and decreased scanner throughput.
  • Increased demand for prostate MRI necessitates efficient imaging solutions.

Purpose of the Study:

  • To provide radiologists with an overview of modern acceleration strategies for prostate MRI.
  • To assess techniques that reduce acquisition time while maintaining PI-RADS v2.1 compliance.
  • To guide the implementation of faster, reliable prostate MRI protocols.

Main Methods:

  • Review of innovations in T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) acquisition.
  • Examination of deep learning reconstruction, optimized 2D fast spin-echo, near-isotropic 3D T2WI, synthetic DWI, and deep learning phase correction.
  • Analysis of evidence supporting accelerated prostate MRI techniques.

Main Results:

  • Deep learning reconstruction for accelerated 2D T2WI shows the most mature clinical evidence.
  • Newer approaches like deep learning phase correction for DWI and 3D T2WI require further validation.
  • Accelerated protocols can be implemented efficiently, retaining PI-RADS-required components in validated cases.

Conclusions:

  • Carefully implemented accelerated prostate MRI improves patient experience and reduces motion artifacts.
  • These protocols can expand access to high-quality prostate MRI without compromising diagnostic reliability.
  • Widespread adoption requires standardized validation, ADC reproducibility assessment, and multicenter evaluation.